churn-prevention
Installation
SKILL.md
Churn Prevention
Production-grade SaaS churn reduction framework covering cancel flow architecture, dynamic save offer mapping, exit survey design, dunning sequence engineering, payment recovery optimization, win-back campaigns, and churn impact modeling. Addresses both voluntary churn (customers who decide to leave) and involuntary churn (customers who leave due to payment failure).
Table of Contents
- Initial Assessment
- Churn Taxonomy
- Cancel Flow Architecture
- Exit Survey Design
- Dynamic Save Offer System
- Dunning Sequence Engineering
- Win-Back Campaign Framework
- Churn Health Scoring
- Metrics and Benchmarks
- Churn Impact Calculator
- Output Artifacts
- Related Skills
Initial Assessment
Required Context
| Question | Why It Matters |
|---|---|
| Current monthly churn rate? (voluntary vs involuntary split) | Determines which lever to pull |
| Do you have a cancel flow, or is cancellation instant/via support? | Determines build vs optimize mode |
| What payment processor? (Stripe, Braintree, Paddle) | Affects dunning implementation |
| Average contract value and billing cycle? | Sizes the save offer budget |
| Current MRR? | Calculates the dollar impact of churn reduction |
| SaaS model? (self-serve vs sales-assisted) | Determines intervention type |
| Do you collect exit reasons today? | Data availability for save offer mapping |
Churn Taxonomy
Voluntary Churn (Customer Decides to Leave)
| Type | Signal | Addressable? |
|---|---|---|
| Value gap | Not getting enough value for the price | Yes -- save offers, feature education |
| Product-market mismatch | Wrong ICP, product does not fit their use case | Partially -- downgrade or pivot |
| Competitor switch | Found a better alternative | Yes -- competitive counter-offers |
| Budget cut | Cannot afford it anymore | Yes -- discount or pause |
| Project completion | Seasonal or project-based need | Yes -- pause option |
| Poor experience | Bad support, bugs, frustration | Yes -- human intervention |
| Never activated | Signed up, never used it | Partially -- reactivation before cancel |
Involuntary Churn (Payment Fails)
| Cause | % of Failed Payments | Recoverable? |
|---|---|---|
| Expired card | 40-50% | Yes -- card updater service |
| Insufficient funds | 20-30% | Yes -- smart retry timing |
| Bank decline (fraud flag) | 10-15% | Sometimes -- customer must contact bank |
| Account closed | 5-10% | No -- customer must provide new card |
| Network error | 5-10% | Yes -- automatic retry |
Cancel Flow Architecture
The 5-Stage Cancel Flow
[Cancel Button] → [Exit Survey] → [Dynamic Save Offer] → [Confirmation] → [Post-Cancel]
Stage 1: Cancel Trigger
- Cancel option is findable (Settings > Account > Cancel). Do not hide it.
- Clicking "Cancel" starts the flow -- it does not immediately cancel the account
- Works on both desktop and mobile
Stage 2: Exit Survey (Required, 1 Question)
Question: "What is the main reason you are cancelling?"
Present as radio buttons (not a dropdown). Maximum 8 options:
| Reason | Internal Code |
|---|---|
| Too expensive for the value I get | PRICE |
| Not using it enough | LOW_USAGE |
| Missing a feature I need | MISSING_FEATURE |
| Switching to a different product | COMPETITOR |
| My project or need ended | PROJECT_END |
| Too complicated to use | COMPLEXITY |
| Just testing, did not plan to keep it | TESTING |
| Other (with optional text field) | OTHER |
Rules:
- Survey is required before showing the save offer (the answer determines the offer)
- One question only. No multi-page surveys.
- Optional free-text field for "Other" and as a supplement to any selection
- Track response distribution monthly to identify systemic issues
Stage 3: Dynamic Save Offer
Map each exit reason to exactly one save offer:
| Exit Reason | Save Offer | Offer Copy |
|---|---|---|
| PRICE | 30-50% discount for 2-3 months | "We'd like to offer you [X]% off for the next [N] months" |
| LOW_USAGE | Pause account for 1-3 months | "Pause your account and come back when you need it" |
| MISSING_FEATURE | Roadmap preview + workaround | "[Feature] is coming in [Q]. Here's how to achieve it now" |
| COMPETITOR | Competitive comparison + discount | "Here's how we compare to [competitor]. Plus [X]% off" |
| PROJECT_END | Pause option | "Pause instead of cancel -- your data stays safe" |
| COMPLEXITY | Free onboarding session | "Let us set it up for you -- free 30-min session with our team" |
| TESTING | No offer -- let them go | "Thanks for trying us out. You're welcome back anytime." |
| OTHER | General retention offer | "Before you go -- we'd love to make this right. [Contact support]" |
Offer presentation rules:
- One clear offer per screen (not multiple choices)
- Quantify the value: "Save $120 over the next 3 months" not "Get a discount"
- CTA: "Accept Offer" vs "Continue Cancelling" (both clearly labeled)
- No countdown timers, no fake urgency
- No guilt-trip copy
Stage 4: Confirmation
If they decline the save offer or there is no offer to make:
┌────────────────────────────────────────┐
│ We're sorry to see you go │
│ │
│ What happens when you cancel: │
│ - Your data is saved for 90 days │
│ - Access continues until [date] │
│ - You can reactivate anytime │
│ │
│ [Yes, Cancel My Account] │
│ [Wait, I Changed My Mind] │
│ │
│ No pre-checked boxes. │
│ No confusing language. │
└────────────────────────────────────────┘
Stage 5: Post-Cancel
| Timing | Channel | Message |
|---|---|---|
| Immediately | Cancellation confirmation + data retention policy + reactivation link | |
| Day 7 | "We miss you" + single CTA to reactivate + what they are missing | |
| Day 30 | Product update + relevant improvement + reactivation offer | |
| Day 60 | Final win-back with strongest offer (if applicable) |
Exit Survey Design
Data Analysis Framework
Track exit survey responses monthly and calculate:
| Metric | Formula | Action Threshold |
|---|---|---|
| Reason distribution | % of cancels per reason | Any reason > 30% = systemic issue |
| Save rate by reason | Saved / Cancel attempts per reason | Any reason < 5% save rate = wrong offer |
| Reason trend | Month-over-month change | Increasing trend = worsening problem |
| Feature gap frequency | Count of "missing feature" with specific feature named | Top 3 missing features = product roadmap input |
Competitive Intelligence from Exit Surveys
When users select "Switching to a different product":
- Ask a follow-up: "Which product are you switching to?" (optional, free text or dropdown)
- Track the top 3 competitors winning your churners
- Feed this data into competitive-teardown skill for quarterly analysis
Dynamic Save Offer System
Offer Economics
| Offer Type | Cost to Business | Save Rate Benchmark | When Profitable |
|---|---|---|---|
| 30% discount (3 months) | 30% of 3 months revenue | 15-25% | If LTV after save > discount cost |
| 50% discount (2 months) | 50% of 2 months revenue | 20-30% | If retained customer stays 6+ months |
| Pause (1-3 months) | $0 (no revenue during pause) | 25-40% | If 50%+ reactivate after pause |
| Free onboarding session | CS team time (~$50-100) | 10-20% | If ARPU > $100/month |
| Downgrade to lower tier | Revenue reduction | 30-50% | If some revenue > no revenue |
| Feature unlock | $0 (already built) | 5-15% | Always profitable |
Save Offer Decision Tree
User selects exit reason →
├── PRICE →
│ ├── Customer ARPU > median? → Offer 30% discount
│ └── Customer ARPU < median? → Offer downgrade to cheaper plan
├── LOW_USAGE →
│ ├── Last login > 30 days? → Offer pause
│ └── Last login < 30 days? → Offer usage tips + discount
├── MISSING_FEATURE →
│ ├── Feature on roadmap? → Share roadmap + workaround
│ └── Feature not planned? → Offer discount or acknowledge gap
├── COMPETITOR →
│ ├── Known competitor? → Show comparison + retention offer
│ └── Unknown competitor? → General retention offer
├── PROJECT_END →
│ └── Always → Offer pause
├── COMPLEXITY →
│ ├── Enterprise/high-value? → Offer dedicated onboarding session
│ └── SMB/low-value? → Offer guided tutorial link
└── TESTING →
└── Always → No offer, let go gracefully
Dunning Sequence Engineering
Failed payments cause 20-40% of total churn. Most of it is recoverable with proper dunning.
Smart Retry Schedule
Do not retry immediately after failure. Cards often recover within 3-7 days.
| Retry | Timing | Why This Timing |
|---|---|---|
| Initial charge | Day 0 | Normal billing cycle |
| Retry 1 | Day 3 | Most card issues resolve within 72 hours |
| Retry 2 | Day 7 | Paycheck cycle alignment |
| Retry 3 | Day 12 | Second paycheck cycle |
| Retry 4 | Day 18 | Final attempt before service action |
| Service action | Day 21 | Downgrade or cancel |
Card Updater Services
Enable automatic card updating to prevent expired card churn:
| Processor | Service | How to Enable |
|---|---|---|
| Stripe | Automatic card updates | Enabled by default on most plans |
| Braintree | Account Updater | Must enable in merchant settings |
| Paddle | Built-in | Automatic |
| Recurly | Account Updater | Configuration required |
Dunning Email Sequence
| Day | Subject Line | Body Focus | CTA |
|---|---|---|---|
| 0 | "Your [Product] payment didn't go through" | Factual, no blame. Card may be expired or funds unavailable. | [Update Payment Method] |
| 3 | "Action needed: update your payment for [Product]" | Remind what they will lose access to. | [Update Payment Method] |
| 7 | "Your [Product] account is at risk" | List features/data they have created. Mild urgency. | [Update Payment Method] |
| 14 | "Final notice: your [Product] access ends in 7 days" | Clear deadline. Offer to help if bank issue. | [Update Payment Method] + [Contact Support] |
| 21 | "Your [Product] account has been paused" | Account status change. Data is safe. Easy reactivation. | [Reactivate Account] |
Email rules:
- Every email links directly to the payment update page (not the dashboard)
- No guilt, no shame. Card failures happen.
- Subject lines are specific (include product name)
- Include the amount owed and the card last 4 digits
- Offer a support channel for customers who need help
Win-Back Campaign Framework
Win-Back Timing
| Window | Success Rate | Approach |
|---|---|---|
| Day 7 post-cancel | 5-10% | Gentle reminder, no pressure |
| Day 30 post-cancel | 3-7% | Product update + offer |
| Day 60 post-cancel | 2-5% | Strongest offer + fresh start |
| Day 90+ post-cancel | 1-3% | Major product change only |
Win-Back Email Sequence
Day 7 Email:
- Subject: "Your [Product] account is waiting for you"
- Body: What they left behind (data, projects, team). One CTA: reactivate.
- No discount. Just value reminder.
Day 30 Email:
- Subject: "Here's what's new in [Product]"
- Body: 2-3 specific improvements since they left. One CTA: reactivate.
- Small incentive: "Come back with 1 month free"
Day 60 Email:
- Subject: "We'd love to have you back -- [offer]"
- Body: Strongest offer (50% off 3 months or extended free period). Clear deadline.
- Final significant outreach attempt.
Churn Health Scoring
Leading Indicators of Churn
| Signal | Weight | Detection |
|---|---|---|
| Login frequency declining (week over week) | High | Usage analytics |
| Feature usage dropping | High | Feature event tracking |
| Support ticket escalation | High | Help desk data |
| NPS response < 7 | High | Survey data |
| Invoice dispute or payment question | Medium | Billing system |
| Champion left the company | High | Contact monitoring |
| Contract renewal in < 90 days | Medium | CRM data |
| Competitor evaluation detected | High | Sales intelligence |
Risk Score Calculation
Risk Score = Sum of (Signal Weight x Signal Present)
0-20: Low risk (monitor)
21-40: Moderate risk (proactive outreach)
41-60: High risk (intervention required)
61+: Critical risk (executive escalation)
Metrics and Benchmarks
Key Metrics
| Metric | Formula | Good | Excellent |
|---|---|---|---|
| Save rate | Customers saved / Cancel attempts | 10-15% | 20%+ |
| Voluntary churn rate | Voluntary cancels / Total customers (monthly) | < 3% | < 1.5% |
| Involuntary churn rate | Failed payment cancels / Total customers (monthly) | < 1.5% | < 0.5% |
| Payment recovery rate | Failed payments recovered / Total failed | 25-35% | 40%+ |
| Win-back rate | Reactivations / Cancellations (90-day window) | 5-10% | 10%+ |
| Exit survey completion rate | Surveys completed / Cancel attempts | > 70% | > 90% |
| Save offer acceptance rate | Offers accepted / Offers shown | 15-25% | 30%+ |
Red Flags
| Signal | Diagnosis | Action |
|---|---|---|
| Save rate < 5% | Offers not matching reasons | Rebuild offer-reason mapping |
| Exit survey completion < 60% | Survey too long or optional | Make it required, 1 question |
| Recovery rate < 20% | Retry logic or emails broken | Audit dunning sequence |
| Single reason > 40% | Systemic product/pricing issue | Escalate to product/leadership |
| Churn rate > 5% monthly | Business is likely contracting | Churn prevention alone will not fix; review ICP + product |
Churn Impact Calculator
Quick Estimate
Monthly MRR at risk = Total MRR x Monthly churn rate
Annual MRR saved by 1% churn reduction = Total MRR x 0.01 x 12
Annual MRR saved by 20% save rate = (Monthly MRR at risk x 0.20) x 12
Example:
MRR: $500,000
Monthly churn: 4% = $20,000/month lost
Reduce to 3% = $5,000/month saved = $60,000/year
Add 20% save rate on remaining = $3,000/month saved = $36,000/year
Total annual impact: $96,000
Output Artifacts
| Artifact | Format | Description |
|---|---|---|
| Cancel Flow Design | 5-stage flow with copy | Complete flow from trigger to post-cancel |
| Exit Survey | Radio button options + mapping | 6-8 reasons with save offer mapping |
| Save Offer System | Decision tree | Reason-to-offer mapping with economics |
| Dunning Sequence | 5-email sequence | Subject lines, body copy, timing, retry schedule |
| Win-Back Campaign | 3-email sequence | Day 7, 30, 60 emails with subject lines and offers |
| Churn Scorecard | Metric table | Current metrics vs benchmarks with gap analysis |
| Impact Model | Revenue calculation | Dollar impact of churn reduction at various improvement levels |
Related Skills
- customer-success-manager -- Use for health scoring, QBRs, and expansion revenue. Not for cancel flow or dunning design.
- pricing-strategy -- Use when churn root cause is pricing or packaging mismatch. Not for save offer design.
- onboarding-cro -- Use when churn traces back to poor activation. If users never experienced value, fix onboarding first.
- referral-program -- Use for acquisition. Churn prevention handles the other end of the funnel.
Tool Reference
1. churn_impact_calculator.py
Purpose: Calculate the revenue impact of churn reduction at various improvement levels.
python scripts/churn_impact_calculator.py --mrr 500000 --churn-rate 4.0 --save-rate 20
python scripts/churn_impact_calculator.py --mrr 500000 --churn-rate 4.0 --save-rate 20 --json
| Flag | Required | Description |
|---|---|---|
--mrr |
Yes | Current monthly recurring revenue in dollars |
--churn-rate |
Yes | Current monthly churn rate as percentage (e.g., 4.0 for 4%) |
--save-rate |
No | Cancel flow save rate as percentage (default: 15) |
--target-churn |
No | Target churn rate as percentage (default: current minus 1) |
--json |
No | Output results as JSON |
2. dunning_sequence_analyzer.py
Purpose: Analyze dunning email sequence effectiveness and recommend retry timing optimizations.
python scripts/dunning_sequence_analyzer.py dunning_data.json
python scripts/dunning_sequence_analyzer.py dunning_data.json --json
| Flag | Required | Description |
|---|---|---|
dunning_data.json |
Yes | JSON file with failed payment and retry data |
--json |
No | Output results as JSON |
Input JSON format:
{
"failed_payments": [
{
"payment_id": "PAY-001",
"amount": 99.00,
"failure_reason": "expired_card",
"retry_attempts": [
{"day": 0, "recovered": false},
{"day": 3, "recovered": false},
{"day": 7, "recovered": true}
]
}
]
}
3. exit_survey_analyzer.py
Purpose: Analyze exit survey responses to identify churn patterns, save offer effectiveness, and systemic issues.
python scripts/exit_survey_analyzer.py survey_data.json
python scripts/exit_survey_analyzer.py survey_data.json --json
| Flag | Required | Description |
|---|---|---|
survey_data.json |
Yes | JSON file with exit survey response data |
--json |
No | Output results as JSON |
--period |
No | Analysis period label (default: "current") |
Troubleshooting
| Problem | Likely Cause | Solution |
|---|---|---|
| Save rate below 5% across all reasons | Save offers do not match exit reasons | Rebuild the exit-reason-to-offer mapping using survey data; run exit_survey_analyzer.py to identify mismatches |
| Exit survey completion under 60% | Survey is optional or too long | Make the single-question survey required before showing the save offer; remove multi-page flows |
| Payment recovery rate below 20% | Retry logic misconfigured or dunning emails not sending | Audit dunning sequence with dunning_sequence_analyzer.py; verify email deliverability and retry schedule |
| Single exit reason exceeds 40% of responses | Systemic product or pricing issue | Escalate to product or leadership; this is not solvable with cancel flow alone |
| Churn rate above 5% monthly | Likely ICP, product-market fit, or pricing problem | Churn prevention alone will not fix this; pair with pricing-strategy and product feedback loops |
| Win-back emails have zero reactivations | Emails not reaching inbox or offers are weak | Check deliverability (SPF, DKIM, DMARC); test stronger offers; verify reactivation links work |
| Involuntary churn rising while voluntary is stable | Card updater not enabled or retry timing is poor | Enable automatic card updating on your payment processor; review retry schedule in dunning_sequence_analyzer.py |
Success Criteria
- Monthly voluntary churn rate below 2.5% (below 1.5% is excellent)
- Monthly involuntary churn rate below 1.0% (below 0.5% is excellent)
- Cancel flow save rate of 15-25% (above 20% is excellent)
- Payment recovery rate of 30%+ on failed payments
- Exit survey completion rate above 80%
- Win-back reactivation rate of 5-10% within 90 days post-cancel
- Save offer acceptance rate above 20% with retained customers staying 6+ months post-save
Scope & Limitations
- In scope: Cancel flow design, exit survey architecture, save offer mapping, dunning sequences, payment recovery, win-back campaigns, churn impact modeling
- Out of scope: Product-market fit analysis, pricing restructuring, ICP redefinition, customer acquisition
- Data dependency: Scripts analyze point-in-time snapshots from JSON input; no real-time CRM integration
- Not predictive ML: All scoring is deterministic and algorithmic -- no machine learning models
- Legal note: Cancel flows must comply with FTC guidelines (US) and consumer protection laws (EU) -- do not make cancellation unreasonably difficult
- Revenue estimates: Impact calculations are projections based on input assumptions, not guarantees
Integration Points
- customer-success-manager -- Feed health scores into churn risk assessment; use churn data to calibrate health score thresholds
- pricing-strategy -- When exit survey data shows PRICE as the dominant reason (>30%), escalate to pricing-strategy for structural pricing review
- onboarding-cro -- When exit survey data shows LOW_USAGE or COMPLEXITY as top reasons, the root cause is often poor activation; fix onboarding first
- revenue-operations -- Pipeline and forecast models should account for churn reduction impact on net revenue retention (NRR)
- referral-program -- Retained customers from save offers are candidates for referral program enrollment after 90 days of continued usage
Related skills